APOLLO: a quality assessment service for single and multiple protein models

Abstract

Summary: We built a web server named APOLLO, which can evaluate the absolute global and local qualities of a single protein model using machine learning methods or the global and local qualities of a pool of models using a pair-wise comparison approach. Based on our evaluations on 107 CASP9 (Critical Assessment of Techniques for Protein Structure Prediction) targets, the predicted quality scores generated from our machine learning and pair-wise methods have an average per-target correlation of 0.671 and 0.917, respectively, with the true model quality scores. Based on our test on 92 CASP9 targets, our predicted absolute local qualities have an average difference of 2.60 Å with the actual distances to native structure.

Availability: http://sysbio.rnet.missouri.edu/apollo/. Single and pair-wise global quality assessment software is also available at the site.

Fig. 1.

A local quality example for CASP9 target T0563. On the left is a plot of predicted local quality scores (colorful line) and actual distance (black line) against residue positions. On the right is the superposition between native structure (grey) and the model. The regions of the model with different local quality are visualized in different colors corresponding to the color of line segments in the plot on the left. Disordered regions are not plotted in the actual distance line.